A Microfabricated Platform to Measure and Manipulate the Mechanics of Engineered Cardiac Microtissues
Bibliographic record
Abstract
Engineered myocardial tissues can be used to elucidate fundamental features of myocardial biology, develop organotypic in vitro model systems, and as engineered tissue constructs for replacing damaged heart tissue in vivo. However, a key limitation is an inability to test the wide range of parameters (cell source, mechanical, soluble and electrical stimuli) that might impact the engineered tissue in a high-throughput manner and in an environment that mimics native heart tissue. Here we used microelectromechanical systems technology to generate arrays of cardiac microtissues (CMTs) embedded within three-dimensional micropatterned matrices. Microcantilevers simultaneously constrain CMT contraction and report forces generated by the CMTs in real time. We demonstrate the ability to routinely produce ~200 CMTs per million cardiac cells (<1 neonatal rat heart) whose spontaneous contraction frequency, duration, and forces can be tracked. Independently varying the mechanical stiffness of the cantilevers and collagen matrix revealed that both the dynamic force of cardiac contraction as well as the basal static tension within the CMT increased with boundary or matrix rigidity. Cell alignment is, however, reduced within a stiff collagen matrix; therefore, despite producing higher force, CMTs constructed from higher density collagen have a lower cross-sectional stress than those constructed from lower density collagen. We also study the effect of electrical stimulation on cell alignment and force generation within CMTs and we show that the combination of electrical stimulation and auxotonic load strongly improves both the structure and the function of the CMTs. Finally, we demonstrate the suitability of our technique for high-throughput monitoring of drug-induced changes in spontaneous frequency or contractility in CMTs as well as high-speed imaging of calcium dynamics using fluorescent dyes. Together, these results highlight the potential for this approach to quantitatively demonstrate the impact of physical parameters on the maturation, structure, and function of cardiac tissue and open the possibility to use high-throughput, low volume screening for studies on engineered myocardium.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".